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Daniel Petry
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initial commit - warm up exercise works
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‎pandas/ex1/__pycache__/warmUpExercise.cpython-36.pyc

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‎pandas/ex1/computeCost.py

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function J = computeCost(X, y, theta)
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%COMPUTECOST Compute cost for linear regression
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% J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
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% parameter for linear regression to fit the data points in X and y
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta
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% You should set J to the cost.
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J = (((X*theta) - y)' * ((X*theta) - y))/(2 * m);
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% =========================================================================
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end

‎pandas/ex1/computeCostMulti.py

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function J = computeCostMulti(X, y, theta)
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%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
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% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
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% parameter for linear regression to fit the data points in X and y
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta
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% You should set J to the cost.
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J = (((X*theta) - y)' * ((X*theta) - y))/(2 * m);
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% =========================================================================
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end

‎pandas/ex1/ex1.py

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## Machine Learning Online Class - Exercise 1: Linear Regression
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# Instructions
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# ------------
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#
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# This file contains code that helps you get started on the
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# linear exercise. You will need to complete the following functions
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# in this exericse:
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#
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# warmUpExercise.m
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# plotData.m
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# gradientDescent.m
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# computeCost.m
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# gradientDescentMulti.m
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# computeCostMulti.m
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# featureNormalize.m
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# normalEqn.m
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#
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# For this exercise, you will not need to change any code in this file,
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# or any other files other than those mentioned above.
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#
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# x refers to the population size in 10,000s
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# y refers to the profit in $10,000s
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#
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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import warmUpExercise
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## ==================== Part 1: Basic Function ====================
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# Complete warmUpExercise.m
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print('Running warmUpExercise ... \n')
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print('5x5 Identity Matrix: \n')
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print(warmUpExercise.warmUpExercise())
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input('Program paused. Press enter to continue.\n')
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### ======================= Part 2: Plotting =======================
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#print('Plotting Data ...\n')
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#data = load('ex1data1.txt')
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#X = data(:, 1) y = data(:, 2)
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#m = length(y) # number of training examples
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#
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## Plot Data
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## Note: You have to complete the code in plotData.m
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#plotData(X, y)
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#
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#print('Program paused. Press enter to continue.\n')
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#pause
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#
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### =================== Part 3: Gradient descent ===================
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#print('Running Gradient Descent ...\n')
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#
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#X = [ones(m, 1), data(:,1)] # Add a column of ones to x
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#theta = zeros(2, 1) # initialize fitting parameters
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#
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## Some gradient descent settings
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#iterations = 1500
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#alpha = 0.01
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#
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## compute and display initial cost
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#computeCost(X, y, theta)
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#
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## run gradient descent
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#[theta, J_history] = gradientDescent(X, y, theta, alpha, iterations)
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#
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#iterVector = 1:iterations
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#
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## print theta to screen
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#print('Theta found by gradient descent: ')
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#print('#f #f \n', theta(1), theta(2))
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#
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## Plot the linear fit
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#hold on
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#plot(X(:,2), X*theta, '-')
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#legend('Training data', 'Linear regression')
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#hold off
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#
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#
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## Predict values for population sizes of 35,000 and 70,000
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#predict1 = [1, 3.5] *theta
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#print('For population = 35,000, we predict a profit of #f\n',...
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# predict1*10000)
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#predict2 = [1, 7] * theta
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#print('For population = 70,000, we predict a profit of #f\n',...
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# predict2*10000)
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#
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#print('Program paused. Press enter to continue.\n')
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#pause
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#
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## Plot the cost function against number of iterations
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#figure
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#plot(iterVector, J_history)
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#xlabel('Iterations') ylabel('Cost')
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#
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#print('Program paused. Press enter to continue.\n')
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#pause
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#
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### ============= Part 4: Visualizing J(theta_0, theta_1) =============
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#print('Visualizing J(theta_0, theta_1) ...\n')
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#
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## Grid over which we will calculate J
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#theta0_vals = linspace(-10, 10, 100)
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#theta1_vals = linspace(-1, 4, 100)
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#
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## initialize J_vals to a matrix of 0's
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#J_vals = zeros(length(theta0_vals), length(theta1_vals))
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#
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## Fill out J_vals
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#for i = 1:length(theta0_vals)
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# for j = 1:length(theta1_vals)
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# t = [theta0_vals(i) theta1_vals(j)]
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# J_vals(i,j) = computeCost(X, y, t)
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# end
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#end
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#
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#
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## Because of the way meshgrids work in the surf command, we need to
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## transpose J_vals before calling surf, or else the axes will be flipped
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#J_vals = J_vals'
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## Surface plot
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#figure
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#surf(theta0_vals, theta1_vals, J_vals)
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#xlabel('\theta_0') ylabel('\theta_1')
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#
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## Contour plot
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#figure
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## Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
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#contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20))
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#xlabel('\theta_0') ylabel('\theta_1')
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#hold on
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#plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2)

‎pandas/ex1/ex1_multi.py

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%% Machine Learning Online Class
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% Exercise 1: Linear regression with multiple variables
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% linear regression exercise.
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%
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% You will need to complete the following functions in this
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% exericse:
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%
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% warmUpExercise.m
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% plotData.m
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% gradientDescent.m
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% computeCost.m
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% gradientDescentMulti.m
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% computeCostMulti.m
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% featureNormalize.m
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% normalEqn.m
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%
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% For this part of the exercise, you will need to change some
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% parts of the code below for various experiments (e.g., changing
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% learning rates).
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%
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%% Initialization
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%% ================ Part 1: Feature Normalization ================
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%% Clear and Close Figures
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clear ; close all; clc
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fprintf('Loading data ...\n');
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%% Load Data
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data = load('ex1data2.txt');
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X = data(:, 1:2);
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y = data(:, 3);
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m = length(y);
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% Print out some data points
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fprintf('First 10 examples from the dataset: \n');
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fprintf(' x = [%.0f %.0f], y = %.0f \n', [X(1:10,:) y(1:10,:)]');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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% Scale features and set them to zero mean
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fprintf('Normalizing Features ...\n');
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[X mu sigma] = featureNormalize(X);
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% Add intercept term to X
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X = [ones(m, 1) X];
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%% ================ Part 2: Gradient Descent ================
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% ====================== YOUR CODE HERE ======================
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% Instructions: We have provided you with the following starter
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% code that runs gradient descent with a particular
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% learning rate (alpha).
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%
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% Your task is to first make sure that your functions -
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% computeCost and gradientDescent already work with
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% this starter code and support multiple variables.
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%
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% After that, try running gradient descent with
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% different values of alpha and see which one gives
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% you the best result.
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%
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% Finally, you should complete the code at the end
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% to predict the price of a 1650 sq-ft, 3 br house.
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%
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% Hint: By using the 'hold on' command, you can plot multiple
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% graphs on the same figure.
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%
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% Hint: At prediction, make sure you do the same feature normalization.
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%
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fprintf('Running gradient descent ...\n');
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% Choose some alpha value
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num_iters = 200;
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% Init Theta and Run Gradient Descent
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theta = zeros(3, 1);
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alpha = 0.1;
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[theta, J_history_1] = gradientDescentMulti(X, y, theta, alpha, num_iters);
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alpha = 0.3;
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theta = zeros(3, 1);
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[theta, J_history_2] = gradientDescentMulti(X, y, theta, alpha, num_iters);
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alpha = 1;
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theta = zeros(3, 1);
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[theta, J_history_3] = gradientDescentMulti(X, y, theta, alpha, num_iters);
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% Plot the convergence graph
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figure;
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plot(1:numel(J_history_1), J_history_1, '-b', 'LineWidth', 2);
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hold on;
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plot(1:numel(J_history_2), J_history_2, '-r', 'LineWidth', 2);
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plot(1:numel(J_history_3), J_history_3, '-k', 'LineWidth', 2);
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xlabel('Number of iterations');
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ylabel('Cost J');
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% Display gradient descent's result
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fprintf('Theta computed from gradient descent: \n');
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fprintf(' %f \n', theta);
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fprintf('\n');
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% Estimate the price of a 1650 sq-ft, 3 br house
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% ====================== YOUR CODE HERE ======================
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% Recall that the first column of X is all-ones. Thus, it does
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% not need to be normalized.
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predictedHouseFeatures = [1650, 3];
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%normalise
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predictedHouseFeatures = (predictedHouseFeatures - mu) ./ sigma;
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%add ones
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predictedHouseFeatures = [1,predictedHouseFeatures];
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%predict
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price = predictedHouseFeatures*theta; % You should change this
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% ============================================================
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fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
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'(using gradient descent):\n $%f\n'], price);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 3: Normal Equations ================
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fprintf('Solving with normal equations...\n');
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% ====================== YOUR CODE HERE ======================
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% Instructions: The following code computes the closed form
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% solution for linear regression using the normal
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% equations. You should complete the code in
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% normalEqn.m
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%
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% After doing so, you should complete this code
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% to predict the price of a 1650 sq-ft, 3 br house.
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%
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%% Load Data
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data = csvread('ex1data2.txt');
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X = data(:, 1:2);
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y = data(:, 3);
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m = length(y);
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% Add intercept term to X
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X = [ones(m, 1) X];
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% Calculate the parameters from the normal equation
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theta = normalEqn(X, y);
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% Display normal equation's result
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fprintf('Theta computed from the normal equations: \n');
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fprintf(' %f \n', theta);
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fprintf('\n');
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% Estimate the price of a 1650 sq-ft, 3 br house
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% ====================== YOUR CODE HERE ======================
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price = 0; % You should change this
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% ============================================================
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predictedHouseFeatures = [1650, 3];
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predictedHouseFeatures = [1,predictedHouseFeatures];
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%predict
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price = predictedHouseFeatures*theta; % You should change this
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fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
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'(using normal equations):\n $%f\n'], price);
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